The invention relates generally to systems for monitoring NOx emissions, and more particularly to systems for obtaining an optimal estimate of the emissions.
Public awareness has increased with respect to the environment, and primary pollutants such as nitrogen oxides and sulfur dioxide are currently regulated in most industries. A great deal of attention in recent years has been spent on addressing the monitoring requirements of these regulations, in order to minimize the discharge of noxious gases into the atmosphere by industrial facilities.
One technique for ensuring correct monitoring of NOx has been to implement continuous emissions monitoring systems (CEMS). A CEM system typically includes a gas analyzer installed either directly in the exhaust stack, or connected via an extractive system which extracts a gas sample from the exhaust stack and conveys it to an analyzer at grade level. Continuous emissions monitoring systems are quite expensive, particularly due to the installation cost and demanding maintenance and calibration requirements of the gas analyzers.
In order to target the challenges associated with CEMS, solutions have been developed that do away with the gas analyzer and instead uses a computer based model for predicting the NOx emissions. The model uses as input a number of monitored parameters from the energy or fuel conversion process, such as temperatures and pressures. These systems are referred to as predictive or parametric emissions measurement systems (PEMS).
There have been PEMS built in the past to predict various combustion and emission parameters from continuous industrial processes and to calculate process or combustion efficiency for compliance reporting and process optimization purposes. Typically, the PEMS is “trained” by monitoring multiple inputs such as pressures, temperatures, flow rates, etc., and one or more output parameters such as, but not limited to, NOx and carbon monoxide. After training, in normal operation, the PEMS monitors only the multiple inputs and calculates estimated output parameter values that closely match the actual pollutant levels. Methodologies used in the past include nonlinear statistical, neural network, eigenvalue, stochastic, and other methods of processing the input parameters from available field devices and to predict process emission rates and combustion or process efficiency. However, the PEMS are complicated, relatively costly, and difficult to implement. The systems also typically require retraining with the support of specialized staff from the system provider to adjust the proprietary model to the real-world conditions encountered in the field.
Therefore, an improved system for obtaining an optimal estimate of NOx emissions is desirable to address one or more of the aforementioned issues.